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Tracking evolving communities in large linked networks.

John Hopcroft1, Omar Khan, Brian Kulis

  • 1Department of Computer Science, Cornell University, Ithaca, NY 14853, USA.

Proceedings of the National Academy of Sciences of the United States of America
|February 6, 2004
PubMed
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This study introduces a method to track changes in large networks by identifying stable "natural communities" within data. This approach helps reveal emerging patterns and evolving structures over time.

Area of Science:

  • Computer Science
  • Network Analysis
  • Data Mining

Background:

  • Tracking changes in large-scale datasets requires robust methods for analyzing evolving network structures.
  • Traditional clustering algorithms often produce unstable results sensitive to minor data perturbations.

Purpose of the Study:

  • To develop a reliable method for tracking temporal changes in large networks.
  • To identify stable substructures (natural communities) within dynamic network data.

Main Methods:

  • Utilized agglomerative clustering on the NEC CiteSeer database (>250,000 papers).
  • Identified stable clusters by analyzing their persistence across multiple clustering runs.
  • Focused on 'natural communities' resilient to perturbations.

Related Experiment Videos

Main Results:

  • Demonstrated that small perturbations significantly alter most clusters in the CiteSeer dataset.
  • Successfully identified stable 'natural communities' that represent underlying network structure.
  • Showcased the ability to track temporal evolution of these communities.

Conclusions:

  • Stable natural communities provide a reliable basis for tracking network evolution.
  • This method enables the identification of emerging communities and dynamic structural changes.
  • The approach is effective for analyzing large, real-world linked network data.